A method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy

Abstract

A virtual resection surgical efficacy evaluation method for hypertrophic obstructive cardiomyopathy based on individualized computational fluid dynamics provides visualized postoperative hemodynamic parameters, predicts postoperative efficacy, identifies the optimal resection plan, and achieves precise and intelligent preoperative assessment. The method includes: acquiring coronary artery enhanced computed tomography (CT) images, obtaining the patient's coronary artery enhanced CT images and outputting and saving them in DICOM format; image data preprocessing, segmenting and reconstructing a three-dimensional model of the left ventricle-outflow tract structure; setting a virtual resection plan for the hypertrophic site; constructing a surgical resection model and importing the model into finite element analysis software for solution; and visualizing the virtual postoperative efficacy. This invention can verify the patient's resection plan and predict efficacy, improving the surgical success rate for patients with hypertrophic obstructive cardiomyopathy.

Description

Technical Field

[0001] This invention relates to a method for evaluating the efficacy of virtual resection surgery, specifically a method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy based on computational fluid dynamics. Background Technology

[0002] Hypertrophic cardiomyopathy (HCM) is a common autosomal dominant inherited disease with a prevalence of 1 in 200 to 1 in 500. It is characterized by ventricular hypertrophy due to mutations in sarcomere protein-encoding genes, sarcomere protein-related genes, or unknown causes. HCM is a pathological condition characterized by abnormal thickening of the myocardium. When hypertrophy affects the basal portion of the interventricular septum, it can lead to obstruction of the left ventricular outflow tract, a condition known as obstructive hypertrophic cardiomyopathy. Patients with mild obstructive hypertrophic cardiomyopathy often remain asymptomatic for many years. Some patients may experience dyspnea, chest pain, or syncope, and even sudden cardiac death, especially young people and athletes, whose risk of sudden death is significantly increased due to strenuous physical labor or intense sports competitions that cause a sudden increase in cardiac output. For patients who do not respond well to medication, early ventricular septal reduction surgery is necessary. Surgical treatment is an effective means to eliminate left ventricular outflow tract obstruction and improve patient prognosis. In surgical practice, accurate preoperative assessment directly affects the rationality of the surgical plan and the predictability of the surgical outcome. Although there are existing literature reports on how to achieve surgical septal resection, the preoperative assessment of patients with hypertrophic obstructive cardiomyopathy still relies almost entirely on the surgeon's extensive surgical expertise and rich experience. This makes it difficult to accurately characterize the complex changes in left ventricular hemodynamics after surgery and predict the surgical efficacy, often failing to achieve the expected good clinical treatment results. Summary of the Invention

[0003] The technical problem to be solved by this invention is to propose a method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy based on individualized computational fluid dynamics. This method can provide visualized postoperative hemodynamic indicators, predict postoperative efficacy, find the optimal resection plan, and achieve accurate and intelligent preoperative assessment.

[0004] The technical solution of this invention is: a method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy based on computational fluid dynamics, comprising:

[0005] Acquisition of enhanced coronary computed tomography (CT) images of the patient's coronary arteries, and outputting and saving them in DICOM format;

[0006] Image data preprocessing, segmentation and reconstruction of a three-dimensional model of the left ventricle-outflow tract structure;

[0007] Based on the hypertrophic area, a virtual resection plan was set up; a surgical resection model was constructed and imported into finite element analysis software for solution.

[0008] Visualize the effects of virtual surgery.

[0009] Furthermore, the specific process of acquiring the coronary artery enhanced computed tomography (CT) image data is as follows: the thoracic aorta is selected as the region of interest, the CT scan value of the vessel in the region of interest is tracked and monitored, and the scan is automatically triggered when the CT scan threshold reaches 300 HU. The obtained coronary artery enhanced CT scan image is output and saved in DICOM format.

[0010] Furthermore, the specific process of image data preprocessing and segmentation reconstruction of the three-dimensional model of the left ventricle-outflow tract structure is as follows:

[0011] Step B1: After importing the coronary artery enhancement computed tomography image sequence into Mimics software, the images are initially selected and located according to the spatial location of the left ventricle in the thoracic cavity; this reduces the number of effective medical images used, thereby reducing the amount of computing memory occupied, increasing image processing efficiency, and saving time.

[0012] Step B2: Select an appropriate threshold for the left ventricular region of interest to generate a mask and reconstruct the entire heart structure;

[0013] Step B3: Use the region growth tool to segment and delete the parts that are not continuous with the left ventricle region, and then use multi-layer and single-layer editing tools to refine and remove the parts outside the left ventricle;

[0014] Step B4: Generate a rough 3D model from the mask, and use the smoothing tool to smooth the surface of the 3D model without distortion, generating a 3D model of the left ventricle of interest, part of the left atrium connected to the left ventricle, and part of the aorta.

[0015] Step B5: Output ASCII STL format to generate an STL model file. Then import the STL model file into Geomagic 2012 software to create pops, simplify, remove nails, and smooth the surface to generate pre-processed model one.

[0016] Furthermore, the specific process of setting up a virtual resection plan and constructing a surgical resection model based on the hypertrophic area is as follows:

[0017] Step C1: First, use Mimics software to observe the location of myocardial hypertrophy and its adjacent anatomical relationships in the reconstructed whole heart structure;

[0018] Step C2: Then, using Geomagic software, select the local wall of the left ventricular outflow tract in the preprocessing model one, that is, the part of myocardial hypertrophy observed in step 3.1), and move the local wall outward by 0mm, 2mm and 5mm respectively, so that the local area of ​​the left ventricular outflow tract is larger than before, and reconstruct three models.

[0019] Step C3: Next, the enlarged left ventricular outflow tract wall is modified, the surface is smoothed, and the mesh doctor operation is performed. Finally, it is saved as an STL (ASCII) model file to generate preprocessing model two, which is used to prepare for the next step of mesh generation.

[0020] Furthermore, the specific process of constructing a surgical resection model and importing the model into finite element analysis software for solution is as follows:

[0021] Step D1: Import the preprocessed model 2 into the SpaceClaim module of Ansys software to complete the processing of the left atrial inlet, aortic outlet and myocardial wall related boundaries, and generate a physical model of the left ventricle-outflow tract structure.

[0022] Step D2: Import the physical model of the left ventricle-outflow tract structure into the Ansys Mesh module for meshing, and process it according to the meshing process to generate a tetrahedral mesh;

[0023] Step D3: The generated tetrahedral mesh is processed using dynamic meshing technology in computational fluid dynamics. A user-defined function (UDF) for wall motion is compiled based on the left ventricular time-volume curve and loaded into the Fluent solver. An algorithm combining elastic smoothing and local reconstruction is used to regenerate the tetrahedral mesh model, specify the ventricular wall motion, control the motion of the wall mesh nodes, and simulate the blood flow process caused by the deformation of the left ventricular outflow tract boundary, which changes the shape of the fluid domain over time, in order to analyze the flow field inside the heart.

[0024] The computational domain is the flow field within the left ventricle-outflow tract, with the inlet boundary being the left atrium, the outlet boundary being the ascending aorta, and the main body of the left ventricle being the deformable wall. Physiological pressure curves extracted from circadapt are used as the boundary conditions for the diastolic atrial inlet and the systolic aortic outlet, respectively, to closely match the hemodynamics under physiological cardiac conditions.

[0025] Step D4: Use Ansys software to simulate the blood flow process caused by the deformation of the fluid domain shape over time due to the deformation of the left ventricle-outflow tract simulated boundary. The simulation is based on the mass and momentum conservation equations, namely the Navier-Stokes equations.

[0026] The differential form of the continuity equation is as follows:

[0027]

[0028] Where ρ is the fluid density and t is time. It is the discreteness, and u is the velocity vector field;

[0029] The momentum conservation equation is expressed as follows:

[0030]

[0031] Where u, υ and w are the velocity vectors in the x, y and z directions, respectively, μ is the dynamic viscosity, P is the pressure, F is the body force, and ρ is the fluid density;

[0032] Step D5: Treat blood as an incompressible Newtonian fluid with a constant dynamic viscosity of 0.0035 kg / m·s, ignore the influence of different disease states on blood viscosity, and have a density of 1060 kg / m3;

[0033] According to the Reynolds index formula: Re = ρυD / μ

[0034] To determine whether the flow is laminar or turbulent, ρ is the blood density, υ is the blood velocity, D is the diameter of the left atrial inlet, and μ is the blood viscosity. The periodic contraction and relaxation of the heart are close to pulsating flow, and a turbulent transient model is used for simulation calculation.

[0035] Step D6: A pressure-based solver was selected, and the SIMPLE scheme was used for pressure-velocity coupled solution. The ventricular wall was set as a non-slip wall. For each outwardly shifted and processed left ventricular outflow tract model, the step size was validated. The final selected time step was 0.005 s, with 640 steps, a calculation cycle of 0.8 s (equivalent to 75 beats / min), a diastolic duration of 0.5 s, and a systolic duration of 0.3 s. The convergence value for each calculation step was set to 1 × 10⁻⁶ for residual monitoring. -4 The number of iterations per iteration was set to 50, and each model had 160 steps per cardiac cycle. The simulation operation lasted for 4 cardiac cycles, totaling 3.2 seconds, to achieve the convergence criterion and obtain accurate simulation results. The output files were exported in .cas and .dat formats. At the same time, the convergence of the mesh and step size was tested to ensure that changes in mesh fineness or time step size would not cause significant differences.

[0036] Furthermore, the specific process of visualizing the virtual postoperative therapeutic effect is as follows:

[0037] Step E1: First, perform preliminary output testing on the simulation results in the Ansys Result module to assess whether they conform to normal physiological conditions;

[0038] Step E2: Then import all Ansys output files into Tecplot software for processing. Tecplot will output the final calculation results, including left ventricular outflow tract blood flow velocity and pressure gradient. The output results will be visualized as images.

[0039] Furthermore, in step B3, the discontinuous part of the left ventricular region refers to the ribs and thoracic vertebrae, and the part outside the left ventricle refers to the pulmonary artery, pulmonary vein, right atrium, and right ventricle.

[0040] Furthermore, the turbulent transient model in step D5 is a turbulent (k-εRNG) transient model.

[0041] This invention proposes a method for virtual resection and evaluation in patients with hypertrophic obstructive cardiomyopathy undergoing surgical resection of hypertrophic interventricular septum myocardium. This method can verify the resection plan and predict the efficacy, thereby improving the success rate of surgery in patients with hypertrophic obstructive cardiomyopathy. First, a three-dimensional model of the left ventricular outflow tract structure is reconstructed using the patient's preoperative coronary artery enhanced computed tomography (CT) data. The model is then reconstructed and connected to finite element analysis software to calculate and simulate the hemodynamics after the virtual resection. This allows for further visualization of postoperative hemodynamic parameters, prediction of postoperative efficacy, and identification of the optimal resection plan, achieving precise and intelligent preoperative assessment. Attached Figure Description

[0042] Figure 1 To invent the overall flowchart;

[0043] Figure 2 Enhanced CT image of the patient's coronary arteries;

[0044] Figure 3 This is a schematic diagram after the initial selection and positioning of the image;

[0045] Figure 4 This is a schematic diagram of a three-dimensional model of the left ventricle.

[0046] Figure 5 A schematic diagram showing the location of myocardial hypertrophy and its adjacent anatomical relationships in the patient;

[0047] Figure 6 Schematic diagrams of the reconstructed model after the thickened wall of the 3D model was moved outward by 0mm, 2mm and 5mm respectively;

[0048] Figure 7 A schematic diagram of the tetrahedral mesh generated after meshing;

[0049] Figure 8 This is a schematic diagram of the change in blood flow velocity in the left ventricular outflow tract over time.

[0050] Figure 9This is a left ventricular pressure cloud map.

[0051] Figure 10 The curve shows the change in pressure gradient of the left ventricular outflow tract over time. Detailed Implementation

[0052] The following is a detailed description of the invention in conjunction with the accompanying drawings:

[0053] like Figure 1 As shown, a method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy based on computational fluid dynamics includes:

[0054] Step 1: Acquisition of coronary artery contrast-enhanced computed tomography (CT) images. The patient's coronary artery contrast-enhanced CT images are obtained, output, and saved in DICOM format. The specific process of acquiring these images involves selecting the thoracic aorta as the region of interest (ROI), tracking and monitoring the CT scan values ​​of the vessels within the ROI, and automatically triggering a scan when the CT scan threshold reaches 300 HU. The scan results are as follows: Figure 2 .

[0055] Step 2: Image data preprocessing, segmentation and reconstruction of the three-dimensional model of the left ventricle-outflow tract structure; the specific process is as follows:

[0056] Step B1: After importing the coronary artery contrast-enhanced computed tomography (CT) image sequence into Mimics software, perform preliminary image selection and localization based on the spatial location of the left ventricle within the thoracic cavity, such as... Figure 3 As shown;

[0057] Step B2: Generate a mask for the left ventricle of interest and reconstruct the entire heart structure, such as... Figure 4 As shown;

[0058] Step B3: Use the region growing tool to segment and delete the parts that are discontinuous with the region of interest, and then use multi-layer and single-layer editing tools to refine and remove the parts outside the left ventricle (not of interest); the discontinuous parts of the left ventricle region of interest mentioned in Step B3 refer to the ribs and thoracic vertebrae, and the parts of the left ventricle region of interest refer to the pulmonary artery, pulmonary vein, right atrium, and right ventricle.

[0059] Step B4: Generate a rough 3D model from the mask, and use the smoothing tool to smooth the surface of the 3D model to generate a 3D model of the left ventricle, part of the left atrium, and part of the aorta of interest.

[0060] Step B5: Output ASCII STL format, then import the STL model file into Geomagic 2012 software for general processing such as creating pops, simplifying, removing nails, and smoothing the surface to generate pre-processed model one.

[0061] Step 3: Based on the hypertrophic area, set up a virtual resection plan; the specific process is as follows:

[0062] Step C1: First, observe the location of myocardial hypertrophy and its adjacent anatomical relationships in the Mimics software, such as... Figure 5 As shown;

[0063] Step C2: In Geomagic 2012 software, select the local wall surface of the left ventricular outflow tract in preprocessing model one, i.e., the observed area of ​​myocardial hypertrophy. Move this local wall surface outward by 0mm, 2mm, and 5mm respectively, thus expanding the local area of ​​the left ventricular outflow tract compared to before, and reconstruct three models, as shown below. Figure 6 As shown;

[0064] Step C3: Modify the enlarged left ventricular outflow tract wall, smooth the surface, perform mesh doctor operations, and finally save it as an STL (ASCII) model file to generate preprocessing model two, which is used to prepare for the next step of mesh generation.

[0065] Step 4: Construct a surgical resection model and import the model into finite element analysis software for solution. The specific process is as follows:

[0066] Step D1: Import the stored preprocessed model 2 into the SpaceClaim module of Ansys software to complete the processing of the left atrial inlet, aortic outlet and myocardial wall related boundaries, and generate a physical model of the left ventricle-outflow tract structure.

[0067] Step D2: Import the preprocessed left ventricular outflow tract structure physical model from the SpaceClaim module into the Ansys 2020R2Mesh module for meshing. Follow the meshing process to generate a tetrahedral mesh, such as... Figure 7 As shown;

[0068] Step D3:

[0069] The generated tetrahedral mesh was processed using dynamic meshing techniques in computational fluid dynamics. A user-defined function (UDF) for wall motion was compiled based on the left ventricular time-volume curve and loaded into the Fluent solver. An algorithm combining elastic smoothing and local reconstruction was used to regenerate the tetrahedral mesh model, specify the ventricular wall motion, control the movement of the wall mesh nodes, and simulate the blood flow process caused by the deformation of the left ventricular outflow tract boundary, which changes the shape of the fluid domain over time, in order to analyze the flow field inside the heart.

[0070] The computational domain is the flow field within the left ventricle-outflow tract, with the inlet boundary being the left atrium and the outlet boundary being the ascending aorta. The main body of the left ventricle is the deformable wall. Physiological pressure curves extracted from circadapt are used as the boundary conditions for the diastolic atrial inlet and the systolic aortic outlet, respectively, to closely match the hemodynamic conditions under cardiac physiological conditions.

[0071] Step D4:

[0072] Ansys software was used to simulate the blood flow process caused by the deformation of the fluid domain shape over time due to the deformation of the left ventricle-outflow tract simulated boundary. The simulation was based on the mass and momentum conservation equations, namely the Navier-Stokes equations.

[0073] The differential form of the continuity equation is as follows:

[0074]

[0075] Where ρ is the fluid density and t is time. It is the discreteness, and u is the velocity vector field;

[0076] The momentum conservation equation is expressed as follows:

[0077]

[0078] Where u, υ, and w are the velocity vectors in the x, y, and z directions, respectively; μ is the dynamic viscosity; P is the pressure; F is the body force; and ρ is the fluid density.

[0079] Step D5: Treating blood as an incompressible Newtonian fluid with a constant dynamic viscosity of 0.0035 kg / m·s, ignoring the effects of different disease states on blood viscosity, and assuming a density of 1060 kg / m³. 3 According to the Reynolds index formula: Re = ρυD / μ, laminar or turbulent flow is determined, where: ρ is blood density, υ is blood velocity, D is the diameter of the left atrial inlet, and μ is blood viscosity. The heart's periodic contraction and relaxation are close to pulsating flow, and a turbulent transient model is used for simulation calculation. Step D6: A pressure-based solver is selected, and the SIMPLE scheme is used for pressure-velocity coupled solution. The ventricular wall is set as a non-slip wall. For each outwardly moved and processed left ventricular-outflow tract model, the step size is verified. The final selected time step is 0.005s, the number of steps is 640, the calculation cycle is 0.8s, equivalent to 75 beats / min, 0.5s for diastole, and 0.3s for systole. The convergence value for each calculation step is: residual monitoring is set to 1×10⁻⁶. -4The number of iterations per cycle was set to 50, with 160 steps per cardiac cycle for each model. The simulation lasted for 4 cardiac cycles, totaling 3.2 seconds, to meet the convergence criteria and obtain accurate simulation results. Output files were exported in .cas and .dat formats. Simultaneously, mesh and step size convergence were tested to ensure that changes in mesh refinement or time step size did not produce significant differences.

[0080] Step 5, the specific process of visualizing the virtual postoperative therapeutic effect is as follows:

[0081] Step E1: First, perform preliminary output testing on the simulation results in the Ansys Result module to assess whether they meet the expected physiological conditions;

[0082] Step E2: Then import all Ansys output files in .cas and .dat formats into Tecplot 360EX2019R1 software, and use Tecplot to output and visualize the final calculation results;

[0083] The calculated indicators include left ventricular outflow tract velocity and pressure gradient. Evaluation of effects: As the degree of outward displacement of the outflow tract wall increases (i.e., the resection thickness increases), the left ventricular outflow tract velocity decreases, the pressure decreases, and the pressure gradient decreases. Figure 8 , 9 As shown in Figure 10. Figure 9 In the diagram, A indicates a wall displacement of 0 mm, B indicates a wall displacement of 2 mm, and C indicates a wall displacement of 5 mm. As the degree of wall displacement in the outflow tract increases, i.e., the resection thickness increases, the pressure in the left ventricular outflow tract decreases. Figure 10 As the degree of outward displacement of the local wall of the outflow tract increases, i.e., the resection thickness increases, the pressure gradient of the left ventricular outflow tract decreases.

[0084] The above are merely specific embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the efficacy of virtual resection surgery in hypertrophic obstructive cardiomyopathy, characterized in that, include: Acquisition of enhanced coronary computed tomography (CT) images of the patient's coronary arteries, output and save in DICOM format; Image data preprocessing, segmentation and reconstruction of the three-dimensional model of the left ventricle-outflow tract structure, generating preprocessed model one; Based on the hypertrophic area, a virtual resection plan is set up, a surgical resection model is constructed, a preprocessing model 2 is generated, and it is saved as an STLASCII model file. A surgical resection model was constructed and imported into finite element analysis software for solution. The pre-processed model was imported into the SpaceClaim module of Ansys software to process the left atrial inlet, aortic outlet, and myocardial wall boundaries, generating a physical model of the left ventricular outflow tract structure. This physical model was then imported into the Ansys Mesh module for meshing, following a meshing process to generate a tetrahedral mesh. The generated tetrahedral mesh was processed using dynamic meshing techniques in computational fluid dynamics. A user-defined function (UDF) for wall motion was compiled based on the left ventricular time-volume curve and loaded into the Fluent solver. An algorithm combining elastic smoothing and local reconstruction was used to regenerate the tetrahedral mesh model, specifying ventricular wall motion and controlling the movement of wall mesh nodes. This simulated the blood flow process, where the fluid domain shape changed over time due to deformation of the left ventricular outflow tract boundary, for analysis of the corresponding heart. The internal flow field was calculated within the left ventricle-outflow tract, with the inlet boundary being the left atrium and the outlet boundary being the ascending aorta. The main body of the left ventricle was a deformable wall. Physiological pressure curves extracted from circadapt were used as boundary conditions for the diastolic atrial inlet and systolic aortic outlet, respectively, to closely match the hemodynamics under physiological cardiac conditions. Ansys software was used to simulate the blood flow process caused by the deformation of the fluid domain shape over time due to the deformation of the left ventricle-outflow tract simulated boundary. The simulation was based on the mass and momentum conservation equations, i.e., the Navier-Stokes equations. Blood was treated as an incompressible Newtonian fluid with a constant dynamic viscosity of 0.0035 kg / m·s. The influence of different disease states on blood viscosity was ignored, and the density was 1060 kg / m³. The Reynolds index was used to determine whether the flow was laminar or turbulent. The periodic contraction and relaxation of the heart approximate pulsating flow, and a turbulent transient model was used for simulation calculation. A pressure-based solver was selected, and the SIMPLE scheme was used for pressure-velocity coupled solution. The ventricular wall was set as a non-slip wall. The step size was verified for each outwardly moved and processed left ventricular outflow tract model. The final selected time step was 0.005 s, the number of steps was 640, the calculation cycle was 0.8 s, equivalent to 75 beats / min, 0.5 s for diastole, and 0.3 s for systole. Convergence value for each calculation step: residual monitoring is set to 1×10. -4 The number of iterations per iteration was set to 50, and each model had 160 steps per cardiac cycle. The simulation operation lasted for 4 cardiac cycles, totaling 3.2 seconds, in order to achieve the convergence criterion and obtain accurate simulation results. The output files were exported in .cas and .dat formats. At the same time, the convergence of the mesh and step size was tested to ensure that there would be no significant difference when the mesh fineness or the time step size was changed. Visualize the effects of virtual surgery.

2. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 1, characterized in that, During the acquisition of coronary artery enhanced computed tomography (CT) image data, the thoracic aorta is selected as the region of interest, and the CT scan values ​​of the vessels in the region of interest are tracked and monitored. When the CT scan threshold reaches 300 HU, the scan is automatically triggered, and the obtained coronary artery enhanced CT scan images are output and saved in DICOM format.

3. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 1, characterized in that, The specific process of segmenting and reconstructing the three-dimensional model of the left ventricle-outflow tract structure is as follows: Step 2.1: After importing the coronary artery enhancement computed tomography image sequence into Mimics software, select and locate the images according to the spatial location of the left ventricle in the thoracic cavity; Step 2.2: Select an appropriate threshold for the left ventricular region, generate a mask, and continue until the entire heart structure is reconstructed; Step 2.3: Use the region growth tool to segment and delete the parts that are not continuous with the left ventricle, and then use multi-layer and single-layer editing tools to refine and remove the parts outside the left ventricle; Step 2.4: Generate a rough 3D model from the mask, and use the smoothing tool to smooth the surface of the 3D model without distortion, generating a 3D model of the left ventricle, part of the left atrium connected to the left ventricle, and part of the aorta. Step 2.5: Output STL ASCII format to generate an STL model file. Then, import the STL model file into Geomagic software for popping, simplification, removal of nails, and surface smoothing to generate the pre-processed model one.

4. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 1, characterized in that, Based on the hypertrophic area, a virtual resection plan was designed, and a surgical resection model was constructed. The specific process is as follows: Step 3.1: First, use Mimics software to observe the location of myocardial hypertrophy and its adjacent anatomical relationships in the reconstructed whole heart structure; Step 3.2: Then, using Geomagic software, select the local wall of the left ventricular outflow tract in the preprocessing model one, that is, the part of myocardial hypertrophy observed in step 3.1), and move the local wall outward by 0mm, 2mm and 5mm respectively, so that the local area of ​​the left ventricular outflow tract is larger than before, and reconstruct three models. Step 3.3: Next, the enlarged left ventricular outflow tract wall is modified, the surface is smoothed, and the mesh doctor operation is performed. Finally, it is saved as an STL ASCII model file to generate the preprocessing model two, which is used to prepare for the next step of mesh generation.

5. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 1, characterized in that, The simulation, based on the mass and momentum conservation equations, i.e., the Navier-Stokes equations, proceeds as follows: The differential form of the continuity equation is as follows: Where ρ is the fluid density and t is time. It is the discreteness, and u is the velocity vector field; The momentum conservation equation is expressed as follows: Where u, υ and w are the velocity vectors in the x, y and z directions, respectively, μ is the dynamic viscosity, P is the pressure, F is the body force, and ρ is the fluid density; The Reynolds index formula is: Re=ρυD / μ Where ρ is blood density, υ is blood flow velocity, D is the diameter of the left atrial inlet, and μ is blood viscosity.

6. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 1, characterized in that it is visualized. The specific process of virtual postoperative efficacy is as follows: Step 5.1: First, perform preliminary output testing on the simulation calculation results in the Ansys Result module to assess whether they conform to normal physiological conditions; Step 5.2: Then import all Ansys output files into Tecplot software for processing. Tecplot will output the final calculation results, including left ventricular outflow tract blood flow velocity and pressure gradient. The final calculation results will be output as a visual image.

7. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 3, characterized in that, In step 2.3, the discontinuous part of the left ventricle refers to the ribs and thoracic vertebrae, while the part outside the left ventricle refers to the pulmonary artery, pulmonary vein, right atrium, and right ventricle.

8. The method for evaluating the efficacy of virtual resection surgery for hypertrophic obstructive cardiomyopathy according to claim 6, characterized in that, The turbulent transient model in step 4.5 is the turbulent transient model k-ε RNG.

Images